Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics.

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ID: 39576
2018
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Abstract
Disorders of consciousness are a heterogeneous mixture of different diseases or injuries. Although some indicators and models have been proposed for prognostication, any single method when used alone carries a high risk of false prediction. This study aimed to develop a multidomain prognostic model that combines resting state functional MRI with three clinical characteristics to predict one year-outcomes at the single-subject level. The model discriminated between patients who would later recover consciousness and those who would not with an accuracy of around 88% on three datasets from two medical centers. It was also able to identify the prognostic importance of different predictors, including brain functions and clinical characteristics. To our knowledge, this is the first reported implementation of a multidomain prognostic model that is based on resting state functional MRI and clinical characteristics in chronic disorders of consciousness, which we suggest is accurate, robust, and interpretable.
Reference Key
song2018prognosticationelife Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Song, Ming;Yang, Yi;He, Jianghong;Yang, Zhengyi;Yu, Shan;Xie, Qiuyou;Xia, Xiaoyu;Dang, Yuanyuan;Zhang, Qiang;Wu, Xinhuai;Cui, Yue;Hou, Bing;Yu, Ronghao;Xu, Ruxiang;Jiang, Tianzi;
Journal eLife
Year 2018
DOI
10.7554/eLife.36173
URL
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